feat: solution for 'Экзамен: RAG-агент с ChromaDB и веб-поиском'

README.md

@@ -1,51 +1,91 @@
 # RAG Agent with ChromaDB and Web Search
 
-This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs web search as a fallback. The agent is written in Node.js and uses only the required dependencies.
+This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector database and performs live web searches to provide up‑to‑date information.
 
 ## Features
 
-- **Vector storage** with ChromaDB (in-memory by default).
-- **Simple embedding** function (placeholder) – replace with a real model for production.
-- **Web search** using DuckDuckGo’s HTML interface.
-- **RAG agent** that retrieves relevant documents or falls back to web search.
+- **Vector store** – Documents are ingested, split into chunks, embedded with OpenAI embeddings, and stored in a persistent ChromaDB collection.
+- **Web search** – Uses DuckDuckGo scraping to fetch recent web snippets for a query.
+- **RAG pipeline** – Combines local document context and web results, then generates an answer with OpenAI GPT‑3.5‑Turbo.
+- **CLI** – Simple command line interface for ingestion and querying.
 
-## Installation
+## Prerequisites
+
+- Python 3.10+
+- An OpenAI API key with access to `text-embedding-ada-002` and `gpt-3.5-turbo`.
+
+## Setup
 
 ```bash
-npm install
+# Clone the repository
+git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
+cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
+
+# Create a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`
+
+# Install dependencies
+pip install -r requirements.txt
 ```
 
+## Configuration
+
+Create a `.env` file in the project root (or set environment variables directly):
+
+```
+OPENAI_API_KEY=sk-...
+CHROMA_DB_PATH=./chromadb
+CHROMA_COLLECTION_NAME=rag_collection
+```
+
+> **Note**: Do not commit your `.env` file or API key to version control.
+
 ## Usage
 
-```bash
-node src/index.js "Your query here"
-```
-
-If no query is provided, it defaults to `"What is ChromaDB?"`.
-
-## Running Tests
+### 1. Ingest Documents
 
 ```bash
-npm test
+python src/main.py ingest path/to/doc1.txt path/to/doc2.txt
 ```
 
+The script will read each file, split it into chunks, generate embeddings, and store them in ChromaDB.
+
+### 2. Query the Agent
+
+```bash
+python src/main.py query "What is the capital of France?"
+```
+
+The agent will:
+
+1. Retrieve relevant chunks from the local vector store.
+2. Perform a DuckDuckGo web search for the query.
+3. Combine both sources of information.
+4. Generate a response using OpenAI GPT‑3.5‑Turbo.
+
 ## Project Structure
 
 ```
 src/
-  index.js        # Entry point
-  agent.js        # RAG agent logic
-  vectorStore.js  # ChromaDB wrapper
-  webSearch.js    # Simple web search helper
-  test.js         # Basic test for vector store
+├── main.py          # CLI entry point
+├── vector_store.py  # ChromaDB ingestion & retrieval
+├── web_search.py    # DuckDuckGo web search
+requirements.txt
+README.md
 ```
 
-## Extending
+## Testing
 
-- Replace the `embed` function in `vectorStore.js` with a real embedding model (e.g., OpenAI, HuggingFace).
-- Persist the ChromaDB collection by configuring the client with a storage path.
-- Add a language model to generate responses from retrieved documents.
+The project can be tested with `pytest` (tests are not included in this minimal example).  
+If you add tests, run:
+
+```bash
+pytest
+```
 
 ## License
 
-MIT
+MIT License
+--- 
+Feel free to extend the agent with additional features such as custom embeddings, different LLMs, or alternative search APIs.